Neuropsychological spatiotemporal pattern recognition

RELATED APPLICATION DATA

0001This application is the U.S. national stage of PCT/IL2007/000639 filed on May 27, 2007, which is based on and claims the benefit of U.S. Provisional Application Nos. 60/899,385 filed on Feb. 5, 2007 and 60/808,107 filed on May 25, 2006, the content of each of which is expressly incorporated herein in its entirety by reference hereto.

FIELD OF THE INVENTION

0002The present invention relates to methods of functional brain imaging and, more particularly, to methods for modeling and/or diagnosing particular neuropsychological functions via spatiotemporal flow patterns among functional brain regions.

BACKGROUND OF THE INVENTION

0003It is known in the field of neuropsychology that behavioral functions are based upon flow among various functional regions in the brain, involving specific spatiotemporal flow patterns. Likewise, behavioral pathologies are often indicated by a change in the patterns of flow. The specific spatiotemporal pattern underlying a certain behavioral function or pathology is composed of functional brain regions, which are often active for many tens of milliseconds and more. The flow of activity among those regions is often synchronization-based, even at the millisecond level and sometimes with specific time delays.

0004Currently, methods for relating behavioral functions to their underlying localized brain activities usually identify discrete participating regions. Although it is known that multiple regions play a role and that the flow from one region to another is important, there are currently very few methods for patterning this flow and relating the patterns to particular tasks, and those methods which do attempt to pattern the flow do not seem to yield sufficiently sensitive and specific identification of the flow patterns underlying specific behavioral functions and pathologies.

SUMMARY OF THE INVENTION

0005There is provided a method for establishing a knowledge base of neuropsychological flow patterns. The method includes obtaining signals from multiple research groups for a particular behavioral process, localizing sources of activity participating in the particular behavioral functions for the research groups, identifying sets of patterns of brain activity for the behavioral functions for the research groups, neuropsychologically analyzing the localized sources and the identified patterns for each of the research groups, wherein neuropsychologically analyzing includes identifying a plurality of possible pathways for the identified sets of patterns, ranking the possible pathways based on likelihood for the particular behavioral process, and reducing the number of ranked possible pathways based on additional constraints. After neuropsychologically analyzing the localized sources, the method further includes creating a set of flow patterns from the neuropsychologically analyzed sources and patterns, for each of the research groups and creating a knowledge base of the flow patterns, wherein the knowledge base is then used as a constraint for the reducing.

0006There is provided, in accordance with additional embodiments of the present invention, a system for neuropsychological brain activity analysis. The system includes a signal collector for collecting signals from a testing subject, a processor having a pattern generator for generating patterns based on the collected signals and a neuropsychological analyzer for translating the generated patterns into neuropsychologically accurate pathways for particular tasks. The system further includes a flow pattern knowledge base having previously determined neuropsychological pathways and a pattern comparator for comparing the collected signals to the flow pattern knowledge base, and an output module for presenting results of the comparison.

0007There is provided, in accordance with additional embodiments of the present invention, a knowledge base of flow patterns comprised of at least one set of flow patterns corresponding to a neuropsychological behavior. The set of flow patterns is established based on identification and neuropsychological analysis of patterns of brain activity from multiple subjects during performance of the neuropsychological behavior, and the set may be compared to a flow pattern obtained from an individual subject. Moreover, the knowledge base may be used to further enhance the identification and neuropsychological analysis.

0008Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

0009The above and further advantages of the present invention may be better understood by referring to the following description in conjunction with the accompanying drawings in which:

0010<figref idref="DRAWINGS">FIG. 1</figref> is a flow chart diagram illustration of an overview of a method of patterning flow in the brain, in accordance with embodiments of the present invention;

0011<figref idref="DRAWINGS">FIG. 2</figref> is a schematic illustration of a system that can be used for data collection in accordance with embodiments of the present invention;

0012<figref idref="DRAWINGS">FIG. 3</figref> is a block diagram illustration showing the formation of a database of data from multiple subjects from different research groups;

0013<figref idref="DRAWINGS">FIG. 4</figref> is a block diagram illustration of a neuropsychological processor, showing its individual components;

0014<figref idref="DRAWINGS">FIG. 5</figref> is a flow chart diagram illustration of a possible method of source localization, in accordance with one embodiment of the present invention;

0015<figref idref="DRAWINGS">FIG. 6</figref> is a block diagram illustration showing the components of a source localizer used in the method of <figref idref="DRAWINGS">FIG. 5</figref>;

0016<figref idref="DRAWINGS">FIG. 7</figref> is an illustration of a peak map for three electrodes;

0017<figref idref="DRAWINGS">FIG. 8</figref> is a schematic illustration of the brain as viewed from above having a first 3-D generated peak map (A) and a second 3-D generated peak map (B);

0018<figref idref="DRAWINGS">FIG. 9</figref> is a flow chart diagram illustration of a method of pattern analysis, in accordance with a first embodiment of the present invention wherein source localization is performed prior to pattern analysis;

0019<figref idref="DRAWINGS">FIG. 10</figref> is a graphical illustration of a step of the pattern analysis of <figref idref="DRAWINGS">FIG. 9</figref>;

0020<figref idref="DRAWINGS">FIG. 11</figref> is a flow chart diagram illustration of a method of pattern analysis, in accordance with another embodiment of the present invention wherein pattern analysis is performed prior to source localization;

0021<figref idref="DRAWINGS">FIG. 12</figref> is a graphical illustration of a raster plot, which serves as a basis of the pattern analysis of <figref idref="DRAWINGS">FIG. 11</figref>;

0022<figref idref="DRAWINGS">FIG. 13</figref> is a schematic illustration of an example of identifying patterns comprised of a time-series of region activations including entailment relations among the regions;

0023<figref idref="DRAWINGS">FIG. 14</figref> is a flow chart illustration of a method of neuropsychological analysis, in accordance with embodiments of the present invention;

0024<figref idref="DRAWINGS">FIG. 15</figref> is an illustration of a matrix representing regions of the brain;

0025<figref idref="DRAWINGS">FIG. 16</figref> is a schematic representation of different flow patterns for identified regions, wherein each of the flow patterns is expected to have different neuropsychological meaning;

0026<figref idref="DRAWINGS">FIGS. 17A-17E</figref> are schematic illustrations of flow patterns showing connectivity between functional regions;

0027<figref idref="DRAWINGS">FIG. 18</figref> is a schematic illustration of a relation structure;

0028<figref idref="DRAWINGS">FIG. 19</figref> is a flow-chart illustration of a method of pattern analysis;

0029<figref idref="DRAWINGS">FIGS. 20A-20C</figref> are schematic representations of the method of <figref idref="DRAWINGS">FIG. 19</figref>;

0030<figref idref="DRAWINGS">FIG. 21</figref> is a block diagram illustration of a system for neuropsychological analysis of an individual, in accordance with embodiments of the present invention;

0031<figref idref="DRAWINGS">FIG. 22</figref> is a block diagram illustration of the signal collector of <figref idref="DRAWINGS">FIG. 21</figref>;

0032<figref idref="DRAWINGS">FIG. 23</figref> is a block diagram illustration of the processor of <figref idref="DRAWINGS">FIG. 21</figref>; and

0033<figref idref="DRAWINGS">FIG. 24</figref> is a schematic illustration of an example of a workstation for neuropsychological analysis, in accordance with embodiments of the present invention.

0034It will be appreciated that for simplicity and clarity of illustration, elements shown in the drawings have not necessarily been drawn accurately or to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity or several physical components may be included in one functional block or element. Further, where considered appropriate, reference numerals may be repeated among the drawings to indicate corresponding or analogous elements. Moreover, some of the blocks depicted in the drawings may be combined into a single function.

DETAILED DESCRIPTION

0035In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be understood by those of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components and structures may not have been described in detail so as not to obscure the present invention.

0036The present invention is directed to methods for spatiotemporal patterning of neuropsychological processes. The principles and operation of methods according to the present invention may be better understood with reference to the drawings and accompanying descriptions.

0037Before explaining at least one embodiment of the present invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.

0038The present invention is directed to a tool which can be used for individual subjects, to analyze their brain activity so as to identify neuropsychological patterns related to behavior, to correlate these identified patterns with particular pathological or non-pathological states, and to aid in therapeutic methods for treating pathologies associated with the identified patterns. Methods for creating such a tool are described first. Methods for using the tool with individual subjects are then described in a later section.

0039Reference is now made to <figref idref="DRAWINGS">FIG. 1</figref>, which is a flow chart diagram illustration of an overview of a method of patterning flow in the brain, in accordance with embodiments of the present invention. The end result of this method is the creation of a knowledge base, which can then be used as reference for later individual trials on subjects. In order to identify flow patterns and create the knowledge base, first data is collected (step <b>90</b>) from multiple subjects. Data collection is done for a particular behavioral function or pathology by collecting data from target groups as well as from control groups. The data collection could be based upon a set of computerized tasks each of the subjects performs, wherein the tasks may include relevant types of stimuli and responses, or data collection may be done during “spontaneous” activity with no such specific task. It should be noted that once the initial knowledge base entry is formed for a specific behavioral function or pathology, data may be continually added to improve the accuracy of the knowledge base. After data is collected for a specific behavioral function or pathology from the relevant target and control groups, one of two analysis lines may be taken. In one embodiment, the sources in the brain for each activity are first localized (step <b>100</b>) from the sampled activity of each subject, as shown with dotted arrows. Source localization involves identifying, from the sampled activity, regions of the brain which underlie it at specific times. Source localization may be performed in various ways, including known methods and novel ones. In some cases, the source localization method will yield various possible solutions, which may then be sorted according to their neurophysiological and neuropsychological likelihood. The next step in this embodiment is pattern analysis (step <b>200</b>), wherein the localized sources are arranged as a time-series for each subject. The elementary events for the time-series could be filtered waveforms, wavelets, markers of wave amplitudes, etc. It should be noted that in this embodiment the focus upon regions which repetitively participate in patterns over many subjects in research groups enables correction of inaccurate source localizations. For example, if an activity is “smeared” in one subject from region A to a neighboring region B, but consistently occurs in region A on many subjects of the research group, only region A will occur in a pattern.

0040In another embodiment, pattern analysis (step <b>200</b>) is done prior to source localization (step <b>100</b>), on the data collected at the initial step. Then, source localization (step <b>100</b>) is done for activities which participate in patterns of interest in order to provide a context for the next step, which is neuropsychological analysis (step <b>300</b>). Once patterns are related to a behavioral function or pathology or to common behavioral sub-functions, which are shared by various higher level behavioral functions (such as, for example, working memory, attention, etc.), the patterns are analyzed (step <b>300</b>) in neuropsychological terms. As will be presented, this analysis is used to correct possible inaccuracies in the source localizations and/or the pattern generation. The pattern analysis further leads to creation (step <b>400</b>) of a knowledge base of entries of known patterns relating to behavioral functions and pathologies or common sub-functions. The knowledge base is also based upon analysis (step <b>95</b>) of published neuropsychological literature. The analysis of published neuropsychological literature is unique in that it includes a description of possible flow patterns among functional brain regions relating to specific behavioral functions, sub-functions or pathologies. Currently, such functional flow information is not generally available in the literature, which usually describes the participation of certain regions in a certain behavioral function or pathology, often without reference to their functional flow relations with other regions in the specific function or pathology or in alternative functions. The knowledge base, in turn, enables improved source localization and analysis of spatiotemporal patterns, by posing constraints regarding possible flow patterns among functional regions. This entire process of improved pattern analysis and localization is automated by evaluating the likelihood of alternative localizations and patterns based on this neuropsychological knowledge base.

0041Reference is now made to <figref idref="DRAWINGS">FIG. 2</figref>, which is a schematic illustration of a system <b>10</b> that can be used for data collection (step <b>90</b>) in accordance with embodiments of the present invention. A subject <b>12</b> has an array of electrodes <b>13</b> placed on his head. Each of electrodes <b>13</b> is in electrical communication with a neuropsychological processor <b>14</b>, the details of which will be described hereinbelow. The electrical communication between electrodes <b>13</b> and neuropsychological processor <b>14</b> can be via wires, as shown in <figref idref="DRAWINGS">FIG. 2</figref>, but can also be wireless. Electrodes <b>13</b> may be placed according to known methods. For example, a 10-20 EEG system may be used, with activity recording from multiple locations, with a reference electrode and a ground. In some embodiments, eye movements (EOG) and muscle movements are recorded as well. Subject <b>12</b> is presented with a stimulus or a set of stimuli, and activity is recorded during a response to the stimulus or stimuli. In alternative embodiments, subject <b>12</b> is not presented with particular stimuli and responses, and activity is recorded during “spontaneous activity” or during particular activities. Many such protocols of stimuli, stimuli-responses, action-related and “spontaneous” activity are known in the art, and may include any stimulus-response neuropsychological tests such as Stroop, Wisconsin, etc; tests may include stimulus-only based tests such as mismatch negativity, BERA, etc; they may include response-only based tests, such as saccade analysis, MRP, etc; and they may include “spontaneous” activity. Activity is sampled for multiple subjects of the target and control groups. In some embodiments, multiple repetitions are averaged and in other embodiments, only single trials are used. In some embodiments, continuous input may be used. The sampled activity is then sent to neuropsychological processor <b>14</b>, where the data are amplified, digitized, recorded and used in an algorithm to determine flow patterns and interpret their neuropsychological meaning.

0042For the purposes of the present invention, any known method for sampling the brain may be used, including MEG, fMRI, PET, optical imaging or any other noninvasive or invasive method and/or combinations thereof. However, the use of EEG or event related potential (ERP) for sampling as it relates to flow patterning has the advantage of high temporal resolution (in the millisecond range) (as does MEG, but which is significantly more expensive). While the tradeoff is in spatial resolution, from a neurophysiological perspective, and while looking for temporal patterns, the temporal resolution is more critical. Spatial resolution of several cm²may be very informative in neuropsychological terms. Furthermore, neighboring regions in the brain generally tend to act in a more synchronous manner and therefore compromise in spatial resolution is often bearable.

0043Reference is now made to <figref idref="DRAWINGS">FIG. 3</figref>, which is a block diagram illustration showing the formation of a database of data from multiple subjects from the different research groups. A research group is defined as a group of subjects with similar behaviors. The behaviors may be actions or activities which are performed in a specific way due to a pathological condition, or the behaviors may be non-pathological actions which the subjects are requested to perform, for example. A research group may also include a control group for comparison with a group having or suspected of having a certain pathological condition or a control group for comparison with a group performing the action. Activity data of subjects are grouped according to research groups (for example, a target group and a control group, as depicted in <figref idref="DRAWINGS">FIG. 3</figref>). Data <b>50</b>, <b>52</b> from each of the research groups are sent to neuropsychological processor <b>14</b>. Although only two research groups are depicted in <figref idref="DRAWINGS">FIG. 3</figref>, it should be readily apparent that multiple research groups may be included. Data from multiple subjects are needed for each research group for generation of patterns by neuropsychological processor <b>14</b>, as indicated by multiple arrows <b>54</b>. Neuropsychological processor <b>14</b> identifies patterns that are repetitive over different subjects in the different research groups based on the entered data. Thus, the output from neuropsychological processor <b>14</b> is a set of characteristic research group patterns, such as characteristic target patterns <b>56</b> and characteristic control patterns <b>58</b> as depicted in <figref idref="DRAWINGS">FIG. 3</figref>. These characteristic patterns are sent to knowledge base <b>16</b> and may be used for later comparison with data from individual subjects. It should be apparent that this is a dynamic system, and that as more patterns are entered, either during research or during testing of individual subjects, the more robust the resulting characteristic patterns will be. Furthermore, knowledge base <b>16</b> can then be used to help determine flow patterns in individual subjects, by sending the known information regarding a particular activity to a flow pattern comparator, as will be described in greater detail hereinbelow.

0044Reference is now made to <figref idref="DRAWINGS">FIG. 4</figref>, which is a block diagram illustration of neuropsychological processor <b>14</b> showing its individual components. Neuropsychological processor <b>14</b> includes a data collector <b>18</b>, a source localizer <b>20</b>, a pattern generator <b>22</b>, and a neuropsychological analyzer <b>24</b>. Data collector <b>18</b> includes a receiver for receiving sampled activity from electrodes <b>13</b>. In one embodiment, shown by the broken arrows, the activity is first processed by source localizer <b>20</b> in such a way that individual locations can be identified. These identified locations are then sent to pattern generator <b>22</b>, which identifies a pattern of flow among the localized sources for the various research groups based on the recordings from electrodes <b>13</b>. These patterns are then sent to neuropsychological analyzer <b>24</b>, which analyzes them in neuropsychological terms by matching with the knowledge base. This matching analysis will be described in further detail herein below. In an alternative embodiment, shown by unbroken arrows, the sampled activity is first sent to pattern generator <b>22</b>, which identifies spatiotemporal patterns among the various electrodes for the various research groups. These identified patterns are then sent to source localizer <b>20</b> so as to identify active locations within the brain for each activity. The identified patterns, which are identified in terms of their localized brain regions, are then sent to neuropsychological analyzer <b>24</b>. In both embodiments, the analyzed neuropsychological flow patterns can then be sent to knowledge base <b>16</b>, to further build up, update and correct the library of patterns for each behavioral function and sub-function. The identified patterns of the neuropsychological analysis are further used to improve the results of the previous source localization and pattern generation by selecting among possibilities and by offering likely corrections.

0045The individual components of neuropsychological processor <b>14</b> and methods of use thereof are now described. As a first step, data collector <b>18</b> collects activity from electrodes <b>13</b>. For example, many different waveforms of varying frequencies and amplitudes over time will be collected for each electrode. All waveforms at all frequencies could then be analyzed at each electrode. Although this method of inclusion of all waveforms at the various frequencies is suggested, it should be readily apparent that other specific waveform definitions with their corresponding analysis methods may be used as well, such as, for example, space filters, blind source separation, or wavelets. Methods for frequency separation are known to those skilled in the art, and may be based on, for example, Fourier transform or different wavelet transforms. The separated bands can then be analyzed for identification of peak areas of activity in the brain, or analyzed in any other manner to form a discrete time-series of events at the various electrodes. Combinations of synchronous activities at different frequencies may also be used, and may help in description of the waveform and the neural pattern. It should be noted that other methods of activity analysis, which are not waveform based are also possible.

0046Reference is now made to <figref idref="DRAWINGS">FIG. 5</figref> and <figref idref="DRAWINGS">FIG. 6</figref>, taken together, where <figref idref="DRAWINGS">FIG. 5</figref> is a flow chart diagram illustration of a possible method of source localization <b>100</b>, in accordance with one embodiment of the present invention, and <figref idref="DRAWINGS">FIG. 6</figref> is a block diagram illustration showing the components of source localizer <b>20</b>, as used in the method of <figref idref="DRAWINGS">FIG. 5</figref>. Waveforms are collected (step <b>102</b>) from either data collector <b>18</b> or from pattern generator <b>22</b>, as described above. A frequency separator <b>30</b> can separate (step <b>104</b>) frequency bands so as to make characteristics such as peaks of each waveform more readily identifiable. Alternatively, any other method, such as wavelet analysis, etc. could be used to separate superpositioned activity. Also any other wave characteristic could be used instead of peaks, such as wave envelope shape, etc. Signal characteristic identifier <b>32</b> then identifies (step <b>106</b>) peaks, wavelets, or other discrete identifiable characteristics from the separated waveforms, and from the identified elements generates (step <b>108</b>) a map for all of the electrodes <b>13</b>, similar to the peak map shown in <figref idref="DRAWINGS">FIG. 7</figref> for three electrodes. As shown in <figref idref="DRAWINGS">FIG. 7</figref>, each of electrodes E₁. . . E₃has its own peaks each of which may be at a different strength. These peaks are identified and displayed in an array, such that it is possible to compare peaks on different electrodes for different points in time. It should further be noted that patterns may be identified from combined activities at different peaks. Furthermore, the combinations of synchronous activities at different frequencies may enable more precise description of the waveform, and may more closely relate to the actual neural pattern. Signal characteristic identifier <b>32</b>, which in the present example is a peak map generator, uses the peak map to generate (step <b>110</b>) a 3-D map of peaks based on the positioning of electrodes <b>13</b>.

0047A space localizer <b>34</b> then uses the 3-D map of peaks to localize (step <b>112</b>) the sources on the brain. It can offer alternative localizations to the pattern identified in the map of the scalp electrodes. Alternatively, source localization can be done by known methods such as low resolution electromotography (LORETA), for example. The localized activity is separated into discrete functional regions either “bottom-up” by patterning among subjects in the same experimental group, “top-down” on the basis of neuropsychological knowledge (i.e. Brodmann's division), or with a combination of both. It should be noted that, as was mentioned previously, localization may be improved via additional information about patterns from knowledge base <b>16</b>.

0048Finally, an adjustor <b>36</b> may correct (step <b>114</b>) for any offsets and for specific pathologies that might result in skewed or missing elements from the pattern. For example, <figref idref="DRAWINGS">FIG. 8</figref> is a schematic illustration of the brain as viewed from above having a first 3-D generated peak map (A) and a second 3-D generated peak map (B). For the purposes of description, it is to be assumed that both peak map A and peak map B were generated for the same activity in two different individuals. In peak map A, peaks are relatively evenly distributed, while in peak map B, there is a higher density on the lateral side. If both of these maps are compared to known maps in a database formed from many such trials, they can be corrected. This type of scenario may result from improperly placed electrodes, or from variations in head anatomy among subjects. Additionally, there may be some scenarios where a particular pathology destroys a portion of the brain. If there are missing peaks in a particular region that can be attributed to such a pathology (based on the database and possibly structural imaging input), corrections can be made for these situations as well.

0049Reference is now made to <figref idref="DRAWINGS">FIGS. 9 and 10</figref>, taken together, where <figref idref="DRAWINGS">FIG. 9</figref> is a flow chart diagram illustration of a method of pattern analysis <b>200</b>, in accordance with a first embodiment of the present invention wherein source localization is performed prior to pattern analysis, and <figref idref="DRAWINGS">FIG. 10</figref> is a graphical illustration of a step of pattern analysis <b>200</b>, as will be described hereinbelow. First, locations found by source localizer <b>20</b> are mapped (step <b>202</b>) onto 3-D grids over time. As shown in <figref idref="DRAWINGS">FIG. 10</figref>, at each time period, a different three-dimensional map of the various locations is generated, showing (step <b>204</b>) signal strengths at locations for a primary time period A and a secondary time period B. Additional time periods may be included as well. Generally, the signal strengths and their spatial distributions change after a period of time (usually in the milliseconds—up to tens of milliseconds range). Thus, for example, at one time, particular localizations may demonstrate particular signal strengths (shown for example in <figref idref="DRAWINGS">FIG. 10</figref> as strengths of 10 and 5 at two locations respectively), while at another time, other localizations may demonstrate different signal strengths (shown for example in <figref idref="DRAWINGS">FIG. 10</figref> as strengths of 12 and 3 respectively). These steps are repeated for all of the subjects within the particular research group. Once patterns from multiple subjects are collected, pattern generator <b>22</b> searches for (step <b>206</b>) repetitive patterns among subjects of the same research group. The patterns involve the timed activation of sets of regions, with temporal, spatial and strength tolerance. This is based upon counting the number of times a particular signal strength at specific spatial location (all, as stated, with tolerance) is obtained at a particular time period, pairs of such events, and so on to larger and larger groups of such events. Thus, a simple counting method is used to determine a pattern wherein patterns of activation of a set of regions, each with its strength/temporal/spatial characteristics that are repetitive among subjects of a certain research group, are identified—all within their dynamic tolerances. It should be readily apparent that the greater the number of inputs (i.e., the number of experimental subjects used), the more robust the pattern analysis will be.

0050Reference is now made to <figref idref="DRAWINGS">FIG. 11</figref> and <figref idref="DRAWINGS">FIG. 12</figref> taken together, where <figref idref="DRAWINGS">FIG. 11</figref> is a flow chart diagram illustration of a method of pattern analysis <b>200</b>′, in accordance with another embodiment of the present invention wherein pattern analysis is performed prior to source localization, and is performed on waveforms directly obtained from electrodes <b>13</b>, (or any other chosen characteristic of the sampled activity), and <figref idref="DRAWINGS">FIG. 12</figref> is a graphical illustration of a raster plot, which serves as the basis of pattern analysis <b>200</b>′, as will be described hereinbelow.

0051First, pattern generator <b>22</b> sets (step <b>203</b>) conditions (such as thresholds) for waveforms obtained from electrodes <b>13</b>. In one embodiment, a binary type of threshold is used, wherein peak values above the threshold are included and values below the threshold are excluded. In another embodiment, a gradual scale may be included. As stated, not only peaks, but also wavelets, or other discrete identifiable elements for each electrode for the particular subject could be utilized. In one embodiment, waveforms which are of varying frequencies are separated out, and peaks are identified (step <b>205</b>) for each frequency at each electrode for each subject. This step is repeated for all electrodes per subject. Next, pattern generator <b>22</b> forms (step <b>207</b>) a raster plot for the full set of electrodes showing peaks over time. An example of a raster plot is depicted in <figref idref="DRAWINGS">FIG. 12</figref>. It should be noted that tolerances for time may be included as well, such that if the peak occurred within the determined tolerance it will be counted. It should further be noted that patterns may be identified from combined activities at different peaks. Furthermore, the combinations of synchronous activities at different frequencies may enable more precise description of the waveform, and may more closely relate to the actual neural pattern. These steps are repeated over multiple subjects and the results of the peak identification of multiple subjects over various frequencies over time are input into a processor which is configured to identify (step <b>209</b>) a pattern of peaks over time for multiple subjects for a particular research group. Specifically, pattern generator <b>22</b> searches for repetitive patterns among subjects of the same research group. The patterns involve the timed activation of sets of electrodes, with temporal, spatial and strength tolerance. This is based upon counting the number of times a particular signal strength is obtained at a particular time period, pairs of such events, and so on to larger and larger groups of such events. Thus, a simple counting method is used to determine a pattern wherein patterns of activation of a set of electrodes, each with its strength/temporal/spatial characteristics that are repetitive among subjects of a certain research group, are identified—all within their dynamic tolerances. It should be readily apparent that the greater the number of inputs (i.e., the number of experimental subjects used), the more robust the pattern analysis will be. Those patterns are later used for comparison, as will be described further hereinbelow. The identified patterns are then sent to source localizer <b>20</b> for source localization.

0052Reference is now made to <figref idref="DRAWINGS">FIG. 13</figref>, which is a schematic illustration of an example of identifying patterns comprised of a time-series of region activations, but which also suggest entailment relations among those regions. This is based on the counting methods, in accordance with embodiments of the present invention. Although the following description refers to identified regions, as in the first embodiment of the present invention wherein source localization is done prior to pattern analysis, it should be readily apparent that similar methods may be used for identifying patterns based on electrode waveforms (or any other activity characteristic), as in the second described embodiment of pattern analysis. As shown in <figref idref="DRAWINGS">FIG. 13</figref>, and as described above with respect to pattern analysis, regions A, B and C are activated (at a certain strength, at a certain time) for the particular research group. However, not all regions become activated for all subjects, and timing may vary. Thus, the total number of times that any combination of the regions (for example, region A and B as shown in line <b>1</b> in <figref idref="DRAWINGS">FIG. 13</figref>) were activated at specific timing and with a specific strength for each research group are noted. The number of participating regions may be any number from one and up and each region can participate more than once without limitation at different times. In the current context, the word “entailment” is defined as a correlative relationship between two events, which may hint at causality. Thus, if event A entails event B, then event A correlates to event B and also might have a causal relationship with event B. Some initial conclusions as to the entailment relationships between region activations (for example, A entails B which entails C versus A entails both B and C independently) may be made. Those initial conclusions are based upon the relative timing among the participating regions. For example, in a first scenario, if one knows the relative timing of C after A over the different subjects in a research group (for example C occurs between t1 and t2 milliseconds after A) and then one looks at activations of C only after A together with B, if the relative timing period does not change significantly by including B, then B does not tend to contribute significant new information with regard to the timing of activation of C after A. On the other hand, in a second scenario, if the relative timing of C seems significantly related to B, then it does contribute new information. The first scenario is most likely indicative of an independent entailment of both B and C by A, while the second scenario is most likely indicative of a dependent one. It should be noted that by taking into account information from knowledge base <b>16</b>, it is possible to improve the sensitivity and specificity of the patterns. In this way, a set of patterns is generated based on multiple subjects for each research group, and this pattern is then updated based on any new inputs or trials that are added later. Another way to achieve a similar result is via comparisons of spatial or strength relations among the regions (instead of temporal relations as presented here). This too will show whether there is additional dependent information or not.

0053Reference is now made to <figref idref="DRAWINGS">FIG. 14</figref>, which is a flow chart illustration of a method of neuropsychological analysis (step <b>300</b>), in accordance with embodiments of the present invention. The purpose of the neuropsychological analysis step (step <b>300</b>) is enhancement of the patterns generated up until this point, as well as their interpretation into neuropsychological terms. Knowledge base <b>16</b> is used to help evaluate specific patterns identified by pattern generator <b>22</b>, and based on the analysis to determine flow patterns including a sequence and duration of activated locations in the brain for each behavioral function and sub-function. These flow patterns are created for both the normal and pathological states, and knowledge base <b>16</b> including these flow patterns are accessible for comparison purposes for evaluating single subjects.

0054Neuropsychological analysis bridges bottom-up and top-down findings. The bottom-up input is a time-series of activities of functional regions which had been previously identified (in the pattern analysis phase) as being repetitive in at least one research group. The top-down input is the knowledge base including functional relations among brain regions. The output of the analysis is a description of possible neuropsychological flow patterns and translation of these flow patterns into neuropsychological terms. Automatic suggestions for correction when the comparisons are imperfect may be included in the output.

0055In the top-down input, several levels of relationship indicators may be used to relate certain regions in the brain to others and thus to form a flow pattern. The first level may include a matrix or other representation of functional brain regions showing relationships between any two regions in the brain. The matrix is created (step <b>302</b>) on the basis of new experimental data, produced in the manner described above or on the basis of data available in the literature, which provides scientific information regarding relationships of certain regions to other regions in particular behavioral functions. The data is rarely directly available in the literature in such a format and often must be deduced from the reports of activation of various specific regions in the specific behavioral function and in other functions and from knowledge regarding anatomical and functional relations among regions. Neuropsychological analyzer <b>24</b> retains an updateable database of these relationships, for example in matrix form. An example of a matrix is shown in <figref idref="DRAWINGS">FIG. 15</figref>, wherein regions R₁. . . R_Nof the brain are represented, down and across. Thus, each box represents a general functional region. Within each box, more specific subcategories of the region are distinguished from one another. As an example, if Region <b>2</b> represents vision as it relates to recognition of the human face, the particular subcategories might include, for example, familiar vs. unfamiliar faces. For a given behavioral function, each region may evoke activity in other regions. With rigorous analysis, information about functional relations among regions can be deduced to a degree from the neuropsychological and neurophysiological literature, and is further compiled by experimental methods such as the ones described in the present application. Thus, pre-existing knowledge about which regions are represented in what order for a particular state can help build the knowledge base, but actual data taken from the present system significantly aids in building the knowledge base, and the knowledge base is adjusted accordingly. It should be readily apparent that a matrix is only one way of depicting flow patterns, but other representations are possible as well. As shown in <figref idref="DRAWINGS">FIG. 15</figref> and stated above, one subcategory may be generally known to lead to a particular region, while another subcategory from the same source region is known to lead to a different region. This information can help in determination of a flow pattern for specific behavioral functions and particularly for sub-functions. Flow patterns can be determined for common sub-functions such as, for example, inhibition, working memory, attention, etc. Alternatively, flow patterns can be determined for particular higher order behavioral functions. The preference among the patterns in the knowledge-base, when comparing them with the patterns from the analysis of previous stages as described above is: (1) patterns for the precise behavioral function at hand, (2) patterns to sub-functions, which are expected in the behavioral function at hand, and (3) patterns which are based upon the functional relations between regions (as in the matrix format).

0056Returning now to the flow-chart illustration of <figref idref="DRAWINGS">FIG. 14</figref>, the above three components, in their preferred order, are used to determine (step <b>304</b>) all possible flow patterns for a given time-series of region activities. For example, as shown in reference to <figref idref="DRAWINGS">FIG. 16</figref>, there may be many different flow patterns involving the identified regions at their specific times of activation, and each pattern is expected to have different neuropsychological meaning. As shown in <figref idref="DRAWINGS">FIG. 16</figref> as an example, if the regions identified are regions A, B, C and D, one possible pathway would be A leads to B which leads to C which leads to D, as shown with solid arrows. Another possibility might be that A leads to B, C and D all together, as shown with short dotted arrows. Yet another possibility might be that A leads to B, and B leads to C and D together, as shown with long dotted arrows. Each of these possibilities might underlie quite a different neuropsychological process. For example, an auditory sensation might activate association in a higher representation area (for example of a voice of a friend), which then in turn might activate association of emotional significance. Alternatively, an activation of another auditory sensation, such as the roar of a tiger might activate by itself the emotional significance representation at its relevant regions and also activate independently the higher representation area. Thus the arrangement of flow among the same regions will have quite a different neuropsychological meaning. Once all possible pathways have been determined, the likelihood of it being one pathway over another is calculated (step <b>306</b>) for the particular behavioral function. This likelihood is based as stated on the three components of the knowledge-base as described above, and is then used to help create (step <b>308</b>) flow patterns and to build the knowledge base <b>16</b>. Automatic suggestions for correction when the comparison to known flow patterns is imperfect may be included in the output.

0057The flow of the algorithm for comparing obtained patterns to the patterns in the knowledge base and for translating the obtained patterns into neuropsychological terms can be as follows. First, on the basis of the pair level comparison (matrix as described above), the time-series is scanned, and all possible relations among regions are marked. The matrix may also include temporal constraints (for example, region A can activate region B at a certain temporal delay with tolerance). Those delays are then imposed in the scan. The output of this stage is either a graph, which is composed of all the possible relations among regions, or a set of isolated sub-graphs, each composed of all the possible relations among its regions. The sub-graphs are separated from one another, because there is no legitimate relation between at least one region in one sub-graph and one region in the other sub-graph. For each sub-graph (or if there is one graph, for the entire graph) all possible combinations of relations (depicted, for example, as arches) which would still span the graph are computed. For example, if a sub-graph is composed of regions A, B and C and it is known from the matrix that at the relevant temporal delays, A can activate B, A can activate C and B can activate C, the possible combinations for the sub-graph would be: (1) A activates B, which activates C; (2) A activates both B and C; and (3) A activates both B and C and the activation of B further activates C. All possible combinations are thus described and counted.

0058A general grade of the match between the bottom-up and the top-down findings is given based on the number of sub-graphs. The less comprehensive the graph (the more sub-graphs there are) the lower the grade.

0059An automatic search is evoked to suggest improvements to the results, so that the graph is more comprehensive. This means that the relations between each 2 sub-graphs are scanned to find possible manners to combine them at a minimal cost, as will be hereby described.

0060The minimal cost corrections could be either via suggestions of correction to the bottom-up process, to the top-down process or both. They are based on the ability to replace a certain region in one (or more) of the sub-graphs, to remove it, or to add a new region. This ability is based on specific considerations, as follows. In correction of the source localization component and with regard to the nature of source localization algorithm employed, the improvement is in finding alternative regions which may have been active and which would connect the sub-graphs. For example, often neighboring regions, which are included, are likely to be erroneously excluded. The analysis is based in this case on the anatomical distance between regions. That is, for example, if a region could be added/replaced which is directly a neighbor of an existing region, it may have a cost of 1; if there is an additional region between them, it may have a cost of 2, etc. Thus, a scan is performed for minimal cost of anatomical distances of additions/replacements/deletions which combines the sub-graphs in accordance to known features of the localization algorithm.

0061In correction of the pattern analysis component and with regard to the nature of the pattern recognition algorithm employed, a similar scan would look for regions that may have been just out of the tolerance ranges (or alternatively for deletion just within tolerance ranges) or just below (or alternatively for deletion just above) threshold and which enable connecting the sub-graphs. Here the cost is based on deviation from thresholds and tolerance margins.

0062In correction of the knowledge-base, it is known that if region A tends to activate region B, which tends to activate region C, then to a lesser degree region A will often directly activate region C. A correction is thus based on adding such “jumps” and the cost is the “jump” distance. Once the knowledge base grows and as it is directly linked to published references, another correction is to point out published references which have shown relations currently excluded from the knowledgebase, or alternatively to point out published references which state that a currently included relation is incorrect. The number of relevant references and their scientific significance (impact factor, etc.) are evaluated as the basis for cost in this case.

0063The sub-graph combinations may also be ranked. The ranking is based on the hereby described preference. Sub-graph combinations which involve paths that are task-related for the relevant task employed are highly ranked. Paths which relate to general sub-functions also gain rank scores (it is possible that more than one sub-function will be found and the rank gains can be combined accordingly). The basic rank is for pair-level relations. Any of those three levels also have inter-level preference rank. Thus, all in all, each sub-graph combination is ranked according to likelihood as well.

0064Finally, according to the translation component of the knowledgebase, each graph, either corrected or basic, is translated into neuropsychological terms. The translation is also based upon the three components—namely task-specific flow patterns, behavioral sub-function flow patterns and pair level.

0065Several examples of flow patterns showing connectivity between functional regions is shown in <figref idref="DRAWINGS">FIGS. 17A-17E</figref> and associated Table 1 which relates functional regions to the numbering on the figures. These diagrams were formed based on published literature. It should be readily apparent that these are merely examples, and do not necessarily represent actual patterns. Moreover, many alternatives may be suggested based on theory and experimental findings. <figref idref="DRAWINGS">FIG. 17A</figref> is a diagrammatic representation of global interrelationships between an action, perception, executive function and attention. <figref idref="DRAWINGS">FIGS. 17B-17E</figref> are more specific diagrammatic representations of perception, executive function, action and attention, showing relationships and interrelationships between different areas of the brain which are functional during these activities. Similar models may be created for particular tasks, behaviors or activities, as described with respect to the present invention.

0066<tables id="TABLE-US-00001" num="00001"><table frame="none" colsep="0" rowsep="0"><tgroup align="left" colsep="0" rowsep="0" cols="1"><colspec colname="1" colwidth="217pt" align="center" /><thead><row><entry namest="1" nameend="1" rowsep="1">TABLE 1</entry></row></thead><tbody valign="top"><row><entry namest="1" nameend="1" align="center" rowsep="1" /></row><row><entry>Modules</entry></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="4"><colspec colname="1" colwidth="98pt" align="left" /><colspec colname="2" colwidth="28pt" align="left" /><colspec colname="3" colwidth="28pt" align="center" /><colspec colname="4" colwidth="63pt" align="left" /><tbody valign="top"><row><entry>Functional module</entry><entry>Hemi</entry><entry>BA</entry><entry>Neuroanatomy</entry></row><row><entry namest="1" nameend="4" align="center" rowsep="1" /></row><row><entry>1. Perception</entry><entry /><entry /><entry /></row><row><entry>1.1. Visual</entry></row><row><entry>1.1.1. Primary visual</entry><entry>X</entry><entry>17</entry></row><row><entry>1.1.2. Secondary visual</entry><entry>X</entry><entry>18</entry></row><row><entry>1.1.3. Tertiary visual</entry></row><row><entry>1.1.3.1. Objective oriented</entry><entry>Lt</entry><entry>19</entry></row><row><entry>1.1.3.2. Subjective oriented</entry><entry>Rt</entry><entry>″</entry></row><row><entry>1.2. Auditory</entry></row><row><entry>1.2.1. Primary auditory</entry><entry>Bi</entry><entry>41</entry></row><row><entry>1.2.2. Secondary auditory</entry><entry>Bi</entry><entry>42</entry></row><row><entry>1.2.3. Tertiary auditory</entry></row><row><entry>1.2.3.1. Objective oriented</entry><entry>Lt</entry><entry>21, 22</entry></row><row><entry>1.2.3.2. Subjective oriented</entry><entry>Rt</entry><entry>″</entry></row><row><entry>1.3. Somatosensory</entry></row><row><entry>1.3.1. Primary somatosensory</entry><entry>X</entry><entry>1, 2, 3</entry></row><row><entry>1.3.2 Secondary somatosensory</entry><entry>X</entry><entry /><entry>Parietal operculum</entry></row><row><entry>1.4. Pain</entry></row><row><entry>1.4.1. Primary pain</entry><entry>X</entry><entry /><entry>Posterior Insula</entry></row><row><entry>1.4.2. Secondary pain</entry></row><row><entry>1.4.2.1. Objective oriented</entry><entry>Lt</entry><entry /><entry>Anterior Insula</entry></row><row><entry>1.4.2.2. Subjective oriented</entry><entry>Rt</entry><entry /><entry>Anterior Insula</entry></row><row><entry>1.5. Heteromodal content</entry></row><row><entry>(a) Objective oriented</entry><entry>Lt</entry></row><row><entry>(b) Subjective oriented</entry><entry>Rt</entry></row><row><entry>1.5.1. Visual-Auditory</entry><entry /><entry>37, 20</entry></row><row><entry>1.5.2. Visual-Somatic</entry><entry /><entry>39</entry></row><row><entry>1.5.3. Global</entry><entry /><entry>38</entry></row><row><entry>1.6. Heteromodal spatial</entry></row><row><entry>1.6.1. Body</entry><entry>X + Rt</entry><entry /><entry>Superior parietal</entry></row><row><entry /><entry /><entry /><entry>Iobule</entry></row><row><entry>1.6.2. Milieu</entry><entry>X + Rt</entry><entry /><entry>Inferior parietal</entry></row><row><entry /><entry /><entry /><entry>Iobule</entry></row><row><entry>1.7. Short term content direction</entry></row><row><entry>1.7.1. Objective oriented</entry><entry>Lt</entry><entry /><entry>Ventral posterior</entry></row><row><entry /><entry /><entry /><entry>cingulum</entry></row><row><entry>1.7.2. Subjective oriented</entry><entry>Rt</entry><entry /><entry>Ventral posterior</entry></row><row><entry /><entry /><entry /><entry>cingulum</entry></row><row><entry>1.8. Short term spatial direction</entry><entry>X</entry><entry /><entry>Dorsal posterior</entry></row><row><entry /><entry /><entry /><entry>cingulum</entry></row><row><entry>1.9. Association</entry></row><row><entry>1.9.1. Objective oriented</entry><entry>Lt</entry><entry /><entry>Hippocamus + para</entry></row><row><entry /><entry /><entry /><entry>hippocampal</entry></row><row><entry>1.9.2. Subjective oriented</entry><entry>Rt</entry><entry /><entry>Hippocamus + para</entry></row><row><entry /><entry /><entry /><entry>hippocampal</entry></row><row><entry>2. Executive function</entry></row><row><entry>2.1. Significance evaluation</entry></row><row><entry>2.1.1. Objective oriented</entry><entry>Lt</entry><entry /><entry>Amygdala</entry></row><row><entry>2.1.2. Subjective oriented</entry><entry>Rt</entry><entry /><entry>″</entry></row><row><entry>2.2. Executive direction</entry></row><row><entry>(a) Content direction</entry><entry>LT</entry></row><row><entry>(b) Spatial direction</entry><entry>RT</entry></row><row><entry>2.2.1. Top level</entry><entry /><entry> 9, 10</entry></row><row><entry>2.2.2. Basic level</entry><entry /><entry>46, 47</entry></row><row><entry>2.3. Outcome prediction</entry></row><row><entry>2.1.1. Objective oriented</entry><entry>Lt</entry><entry /><entry>Ventromesial</entry></row><row><entry /><entry /><entry /><entry>prefrontal cortex</entry></row><row><entry>2.1.2. Subjective oriented</entry><entry>Rt</entry><entry /><entry>Ventromesial</entry></row><row><entry /><entry /><entry /><entry>prefrontal cortex</entry></row><row><entry>3. Action</entry></row><row><entry>3.1. Abstract action</entry></row><row><entry>3.1.1. Content action</entry><entry>Lt</entry><entry>44, 45</entry></row><row><entry>3.1.2. Spatial action</entry><entry>Rt</entry><entry>″</entry></row><row><entry>3.2. Implementation</entry><entry>X</entry><entry /><entry>Medial cingulum</entry></row><row><entry>3.3. Complex action</entry></row><row><entry>3.3.1. Body</entry><entry>X</entry><entry> 6</entry></row><row><entry>3.3.2. Eyes</entry><entry>X</entry><entry> 8</entry></row><row><entry>3.4. Basic action</entry><entry>X</entry><entry> 4</entry></row><row><entry>3.5. Action maintenance</entry><entry>II</entry><entry /><entry>Cerebellum</entry></row><row><entry>4. Attention</entry></row><row><entry>4.1. Process selection</entry></row><row><entry>4.1.1. Executive selection</entry></row><row><entry>4.1.1.1. Content selection</entry><entry>Lt</entry><entry /><entry>Ventral basal</entry></row><row><entry /><entry /><entry /><entry>ganglia</entry></row><row><entry>4.1.1.2. Spatial selection</entry><entry>Rt</entry><entry /><entry>Ventral basal</entry></row><row><entry /><entry /><entry /><entry>ganglia</entry></row><row><entry>4.1.2. Implementation selection</entry><entry>X</entry><entry /><entry>Dorsal basal</entry></row><row><entry /><entry /><entry /><entry>ganglia</entry></row><row><entry>4.2. Perceptual attention</entry><entry>U</entry><entry /><entry>Locus Ceruleus</entry></row><row><entry>4.3. Executive attention</entry><entry>U</entry><entry /><entry>Ventral tegmental</entry></row><row><entry /><entry /><entry /><entry>area</entry></row><row><entry>4.4. Action attention</entry><entry>U</entry><entry /><entry>Raphe nuclei</entry></row><row><entry namest="1" nameend="4" align="center" rowsep="1" /></row></tbody></tgroup></table></tables>

0067A method of pattern recognition in accordance with additional embodiments of the present invention is now described.

0000Definitions

0000<ul id="ul0001" list-style="none"><li id="ul0001-0001" num="0068">Entity—either (1) a basic symbol in the input order series, which is, in the current application, an active functional area, or (2) a pair as it is defined below. The first entities are basic symbols and as the algorithm runs, new entities are formed, which can be composed of 2 basic symbols, and then of a basic symbol and a previous pair, 2 previous pairs and so on.</li><li id="ul0001-0002" num="0069">Occurrence—a specific event of an entity in the order series. Each entity, whether basic or complex, can occur in many entries. As will be presented below, an occurrence of a complex entity can spread over more than one entry. As a means of convection, complex entities will be considered as occurring in the first entry they involve. As will be presented below, they will include the necessary information regarding the other entries involved in them.</li><li id="ul0001-0003" num="0070">Pair—a relation between 2 entities. The pair could be built of entities in the same order entry, or of entities from different order entries—for example with a delta of 1 entry, 2 entries etc. Thus, the definition of each pair involves also the delta between the order entries. If the 2 entities are of the same entry, the delta is 0. Thus the pair (i,j|0) means a relation between entity i and between entity j in the same entry; the pair (i,j|1) means a relation between entity i and entity j in a consecutive entry; the pair (j,i|1) means a relation between entity j and entity i in a consecutive entry. Note that pair (j,i|0) is the same as pair (i,j|0) because at the same entry, there is no order difference.</li><li id="ul0001-0004" num="0071">Ancestor entities—defined for complex entities, composed of at least one pair, these are the entities which are paired with other ancestor entities at any step in the process of creating the complex entity. For example, suppose entities i &amp; j were paired as (i,j|0) and then paired with entity k as ((i,j|0),k|0). Suppose also that entities l &amp; m were paired as (l,m|0). Now suppose that both complex entities were paired as (((i,j|0),k|0), (l,m|0)|0). Let us term this new entity—x. The ancestor entities of x are then: 1.((i,j|0),k|0), 2.(I,m|0), 3.(i,j|0), 4.k; 5.l, 6.m, 7.i &amp; 8.j.</li><li id="ul0001-0005" num="0072">Independent pair—a pair that reflects a relation which does not result from other pairs.</li><li id="ul0001-0006" num="0073">Dependent pair—a pair that reflects a relation which results from other pairs. For example, if i, j and k are entities, which hold the following relations: i→j→k, then (i,j|Δ_ij) and (j,k|Δ_jk) are independent pairs, while (i,k|Δ_ij+Δ_jk) is a dependent pair. <br /> Statistical significance of a pair (i,j|Δ_ij)—If entity i occurs x_itimes in an order series, which includes n entries altogether, and entity j occurs x_jtimes in the same order series, then the probability of occurrence of entity i is x_i/n and the probability of occurrence of entity j is x_j/n. The probability of random co-occurrence of entity i &amp; j, with any Δ_ij, in the same order series entries, p_ijis the product of (x_i/n) &amp; (x_j/n). This is provided that Δ_ijis small enough when compared to n and neglecting near edge distortions, by which occurrences of i in the last Δ_ijentries of the order series could not be followed by j. If the real co-occurrence of entities i &amp; j, with a specific Δ_ij, in the same order series entry is x_ijΔij, it is possible to use the binomial distribution to evaluate the statistical likelihood of it. The formula of the binomial distribution is </li></ul>

0074<maths id="MATH-US-00001" num="00001"><math overflow="scroll"><mrow><mrow><mi>F</mi><mo>⁡</mo><mrow><mo>(</mo><mrow><mi>x</mi><mo>,</mo><mi>p</mi><mo>,</mo><mi>n</mi></mrow><mo>)</mo></mrow></mrow><mo>=</mo><mrow><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>0</mn></mrow><mi>x</mi></munderover><mo>⁢</mo><mrow><mrow><mo>(</mo><mtable><mtr><mtd><mi>n</mi></mtd></mtr><mtr><mtd><mi>i</mi></mtd></mtr></mtable><mo>)</mo></mrow><mo>⁢</mo><msup><mrow><mo>(</mo><mi>p</mi><mo>)</mo></mrow><mi>i</mi></msup><mo>⁢</mo><msup><mrow><mo>(</mo><mrow><mn>1</mn><mo>-</mo><mi>p</mi></mrow><mo>)</mo></mrow><mrow><mo>(</mo><mrow><mi>n</mi><mo>-</mo><mi>i</mi></mrow><mo>)</mo></mrow></msup></mrow></mrow></mrow></math></maths><img file="US8320649B2_D0001.tif" /><br /> in our case: x is x_ijΔij, p is p_ijand n is n. The value computed denotes the likelihood that x_ijco-occurrences will occur randomly. The smaller this value the greater the likelihood that the event is not random. We use an arbitrary threshold of 0.001 to define significant pairs. Note that both independent and dependent entities can be significant.

0075The goal of this method of pattern analysis is to start from the order series data and to expose the activity relations structure, or the relation patterns. As an example, suppose the symbols in a given dataset are i,j &amp; k. The activity relation patterns, or the activity relations structure, in the dataset are precisely the following: symbol i entails symbol k in the same order entry, with a likelihood of 0.5, and symbol i together with symbol j in the same order entry entail symbol k in the following order entry, with a likelihood of 0.75. Furthermore, suppose that symbol i and symbol j occur spontaneously with a random likelihood of 0.4 and 0.8 correspondingly and symbol k does not occur spontaneously. This relations structure might lead to the following order series:

0076<tables id="TABLE-US-00002" num="00002"><table frame="none" colsep="0" rowsep="0" pgwide="1"><tgroup align="left" colsep="0" rowsep="0" cols="2"><colspec colname="offset" colwidth="28pt" align="left" /><colspec colname="1" colwidth="280pt" align="center" /><tbody valign="top"><row><entry /><entry namest="offset" nameend="1" align="center" rowsep="1" /></row><row><entry /><entry>Entry</entry></row></tbody></tgroup><tgroup align="left" colsep="0" rowsep="0" cols="9"><colspec colname="1" colwidth="28pt" align="center" /><colspec colname="2" colwidth="35pt" align="left" /><colspec colname="3" colwidth="35pt" align="left" /><colspec colname="4" colwidth="35pt" align="left" /><colspec colname="5" colwidth="35pt" align="left" /><colspec colname="6" colwidth="35pt" align="left" /><colspec colname="7" colwidth="35pt" align="left" /><colspec colname="8" colwidth="35pt" align="left" /><colspec colname="9" colwidth="35pt" align="left" /><tbody valign="top"><row><entry>Symbol</entry><entry>1</entry><entry>2</entry><entry>3</entry><entry>4</entry><entry>5</entry><entry>6</entry><entry>7</entry><entry>8</entry></row><row><entry namest="1" nameend="9" align="center" rowsep="1" /></row><row><entry>i:</entry><entry>ACTIVE</entry><entry /><entry>ACTIVE</entry><entry /><entry /><entry>ACTIVE</entry><entry /><entry>ACTIVE</entry></row><row><entry>j:</entry><entry>ACTIVE</entry><entry>ACTIVE</entry><entry>ACTIVE</entry><entry /><entry>ACTIVE</entry><entry /><entry>ACTIVE</entry><entry>ACTIVE</entry></row><row><entry>k:</entry><entry /><entry>ACTIVE</entry><entry /><entry>ACTIVE</entry><entry /><entry>ACTIVE</entry></row><row><entry namest="1" nameend="9" align="center" rowsep="1" /></row></tbody></tgroup></table></tables>

0077While the current algorithm presented is aimed for an order series data, the theoretical principles of the algorithm, which will be explained below, would be applicable to a time series, which basically involves tolerance in the precise timing among related occurrences of entities. Furthermore, while the current activity analyzed is in terms of active/inactive, the theoretical principles are also applicable to a scale of possible values for the occurrences of an entity, which would require strength tolerance. Note that, as is evident from the example above, in a finite dataset, there could be various possible data structures. It is possible to add a possibility to form all alternatively plausible relation structures for a given dataset along with rankings of likelihood.

0078A relation structure is divisible into pairs and simple groups. Reference is now made to <figref idref="DRAWINGS">FIG. 18</figref>, which is a schematic illustration of a relation structure. Each relation has its own strength of significance and temporal delta. Furthermore, if the temporal delta is more than 0, the relation also has temporal direction. However, for the purpose of the current point, it is possible to ignore those characteristics. A relation structure is composed of pairs of entities and of simple groups of entities. The pairs are evident in the presented structure—for example, (j,k); (k,m); (j+k,n+o); etc. A simple group is composed of entities, which tend to occur together. For example in the presented structure, the node uniting i, j &amp; l marks a group. Note that j,k &amp; m are also connected and in a sense occur together, but this results from the pairs (j,k) and (k,m) which happen to co-occur. On the other hand, the entities comprising a simple group, or in the example, the entities i,j &amp; l, occur together significantly beyond random co-occurrence of the various pairs composing it. In terms of dependent probabilities, in the trio j, k &amp; m, if it is known that k occurred, knowing further that j occurred as well, does not increase the likelihood for m to occur. However, in the trio i, j &amp; l, knowing that any entity occurred together with any other entity increases the likelihood of the 3^rdentity to occur. The dependent probability considerations are extendable to simple groups larger than trios in a straightforward manner.

0079More complex structures are also divisible into pairs and simple groups. Already the structure presented involves a pair composed of prior pairs ((j,k), (n,m)). More complex relations are also divisible in a similar manner to pairs and simple groups. Note that multiple relations among basic symbols or complex entities are also possible. For example, in the above structure, entities i &amp; l could have been also paired directly in addition to the group they form with j. Nevertheless, this additional relation is still a pair. Also note that a negative relation between entities, complex or basic, is also possible. A negative relation means that the involved entities tend to co-occur significantly less than what would have been expected randomly. Or in other terms, if one of those entities or group of entities occurs, the likelihood of the other entailed entity or group of entities to co-occur reduces significantly. Again a negative relation is still a pair or a simple group relation.

0080Thus, it is possible to divide relation structures to pairs of entities and simple groups, down to the level of basic symbols. Furthermore, note that each simple group could be described in terms of pairs, where at first 2 entities are paired to form the core pair of the group, then this core pair is paired with another entity to form the core trio and so on. Thus, it is possible to divide any relations structure consistently to 2 parts, down to the level of the basic symbols. This, however, means that for a given order series dataset, it is possible to expose the underlying relations structure on the basis of pairing from the basic symbols upwards. It is only necessary to pair correctly the algorithm presented here. Choice of threshold, sample size and method for calculating statistical significance will all determine sensitivity and/or specificity of the method.

0081In order to maximize correct results, strongest relation pairs are included as new entities. Therefore it should be noted that if there are 2 independent relations—for example (i,j) and (j,k) which underlie to a dependent relation (i,k), the 2 independent relations are always stronger then the dependent one. This is because the dependent relation is a mere random co-occurrence of the independent relations and its probability is the product of the probabilities of the independent relations and therefore it is smaller. Thus, if pairs are selected in the algorithm's ordered pairing process, it means they are independent. We further reduce from the dependent pair count the occurrences which result from the co-occurrence of the 2 independent pairs and thus, if the dependent pair does not occur significantly beyond those occurrences, it will be excluded as insignificant. It will be included only as dependent by later unification of the 2 independent relations. Note that if it occurs significantly also independently beyond those occurrences, it is selected as independent and regains all the reduced occurrences. Lastly, as will be described in the below, for each newly selected pair, its relations with the other entities are computed. If this new pair (i,j) relates strongly with the entity k, it will form a simple group ((i,j),k). However, if k relates more strongly to one of the pair's comprising entities, it will relate to it and the 2 pairs could later be united—for example, ((i,j), (j,k)).

0082Reference is now made to <figref idref="DRAWINGS">FIG. 19</figref>, which is a flow-chart illustration of a method of pattern analysis, and to <figref idref="DRAWINGS">FIGS. 20A-20C</figref>, which is a schematic representation of the method of <figref idref="DRAWINGS">FIG. 19</figref>. First, a basic list of entities is built (step <b>502</b>). This list of entities is a list of basic symbols. Next, an ordered list of all significant pair relations between various entities in the basic list is computed (step <b>504</b>). As defined above, pairs could involve either entities from the same entry or entities from different entries, with a certain delta between them. The list is ordered, so that the most significant pair comes first, followed by the second strongest and so on. Note that only significant pairs, below the significance threshold, are included in the list. Next, the strongest pair is selected (step <b>506</b>) from the top of the list, and added to the entities' list as a new entity. It is also removed from the significant pairs list. Next, occurrences of the strongest pair in the relevant entries of the order series are marked (step <b>508</b>). This marking also includes occurrences which might have been previously designated inactive. This is because once a pair is selected, it is allowed to pair with other entities even in occurrences where it is a merely a subset of previously discovered entities. Next, the statistical significance of the pairs it forms with other previous entities is calculated (step <b>510</b>) according to their co-occurrence in the order series. Next, if the new entity is more than a pair of two basic symbols (ie, includes at least one previous pair), then a first ancestors list and a second ancestors list are built (step <b>512</b>). The first ancestors list includes all of the entities which are ancestors to the first entity of the new pair. The second ancestors list includes all of the entities which are ancestors to the second entity of the new pair. All pairs of entities from the first and second ancestors lists are reviewed. For each ancestor pair, if the pair is in the significant pairs list, then the order series is reviewed and for every entry which includes an occurrence of the new entity, the ancestors pair is designated (step <b>514</b>) as inactive. Note that it may have already been inactive if it was already an ancestors pair in a previous iteration. Ancestor pair occurrences are recounted as are counts of its two comprising entities, without the inactive occurrences. Note that the count of occurrences of the comprising entities is not generally reduced, but only locally reduced, in relation to the specific inter-ancestors pairs. In their relations with other entities, which are not on the 2^ndancestors list, the ancestor entities counts and co-occurrence counts stay the same. Ancestors pair significance is re-computed, and its order in the significant pairs list is updated accordingly or removed from the list. If ancestor pairs are not significant, the pair is discarded (step <b>516</b>).

0083In some embodiments, troubleshooting and automated evaluation of the knowledge base, source localization or pattern analysis may be done by comparing analyzed patterns to known patterns already in the knowledge base. For example, if there is a slight discrepancy in region, wherein an analyzed pattern includes a region or regions which is different than previously determined and stored patterns, if the regions are neighboring it is likely a source localization problem. Alternatively, if the regions are distant from each other, it is likely a knowledge base problem. Another example involves a correction of the knowledge base. According to the matrix knowledge base, each of two regions are either directly related, or are related via a certain number of intervening regions. If this evaluated distance at the knowledge base level is in discrepancy when compared to the identified patterns in a repetitive manner, which cannot be explained by alternative corrections (as the one suggested above to localization and others), then there is likely a knowledge base imperfection which requires correction. Yet another example is correction of the identified flow pattern analysis by higher resolution search for a specific region, which is predicted from the knowledge base and might have just fallen short of the threshold or tolerance parameters set.

0084The knowledge base <b>16</b> created in the manner described above is used in the system of the present invention, as will be described hereinbelow with respect to <figref idref="DRAWINGS">FIG. 21</figref>. Reference is now made to <figref idref="DRAWINGS">FIG. 21</figref>, which is a block diagram illustration of a system <b>600</b> in accordance with embodiments of the present invention. System <b>600</b> includes a signal collector <b>602</b> configured to collect signals from a testing subject <b>603</b>, a processor <b>604</b> for processing the signals, and an output module <b>606</b> for displaying the results of the processed signals. Reference is now made to <figref idref="DRAWINGS">FIG. 22</figref>, which is a block diagram illustration of signal collector <b>602</b> shown in greater detail. In some embodiments, signal collector <b>602</b> includes electrodes <b>13</b> to be placed on the head of testing subject <b>603</b>, and an amplifier <b>608</b> for amplification of signals received from electrodes <b>13</b> in response to an activity or task by testing subject <b>603</b>. In some embodiments, collector <b>602</b> further includes tasks. It should be readily apparent that other types of signals may be collected and that signal collector <b>602</b> is not limited to the description herein. For example, signal collector <b>602</b> may include fMRI, PET, optical imaging, MEG or any system or method (and their combinations) for obtaining information related to brain function in a human. In some embodiments, subject <b>603</b> is not presented with particular stimuli and responses, and activity is recorded during “spontaneous activity” or during particular activities. Many such protocols of stimuli, stimuli-responses, action-related and “spontaneous” activity are known in the art, and may include any stimulus-response neuropsychological tests such as Stroop, Wisconsin, etc; tests may include stimulus-only based tests such as mismatch negativity, BERA, etc; they may include response-only based tests, such as saccade analysis, MRP, etc; and they may include “spontaneous” activity.

0085Reference is now made to <figref idref="DRAWINGS">FIG. 23</figref>, which is a block diagram illustration of processor <b>604</b>, showing the components in greater detail. Processor <b>604</b> includes an input adjustor <b>610</b>, a pattern comparator <b>612</b> and a copy of knowledge base <b>16</b> created from experimental and published information, as described above. Input adjustor <b>610</b> is configured to adjust input from signal collector <b>602</b> so that it conforms to the flow pattern information found in knowledge base <b>16</b>. Thus, in one embodiment, input adjustor <b>610</b> includes a source localizer <b>20</b> and is configured to perform source localization so as to identify regions of the brain being activated by the activity or task performed by testing subject <b>603</b>. In another embodiment, input adjustor <b>610</b> is configured to identify peaks, wavelets, or other discrete identifiable elements over time for electrodes <b>13</b>. In this second embodiment, the knowledge base patterns are also described at this electrode level. Pattern comparator <b>612</b> then takes the adjusted input and compares it to flow patterns included within knowledge base <b>16</b>. Pattern comparator <b>612</b> is configured to identify a pathology or normal state based on comparison of the adjusted input and the stored information regarding pathological or normal patterns. Moreover, pattern comparator <b>612</b> may translate the determined patterns even if parts of the patterns do not relate to a specific pathology, by parsing the activities according to their likelihoods of matches with stored flow patterns, as will be described further hereinbelow.

0086In parsing, over time and as more regions are introduced, the possibilities of patterns to match up with are sharpened. Thus, for example, at a single timing with a few regions, many different patterns may fit the time-series of region activations obtained from the single subject. However with more sampled regions over time, certain patterns become more likely. It should be noted that even when particular regions or sequences of electrodes are identified, timing at the particular regions or electrodes is important in distinguishing between flow patterns. The process of parsing eventually results in a matching up of the obtained patterns with saved patterns from the database. Similarly to the above description regarding the neuropsychological analysis, the parser may work on several levels, wherein at a first level, combinations of pairs of regions are identified. At a second level, the parser identifies general behavior based on flow patterns for particular behavior sub-functions. At its most specific level, the parser can identify patterns directly relating to specific behavioral functions, such as an activity or task being performed by testing subject <b>603</b>. The algorithm described with respect to the development of the research tool may also be used for neuropsychological analysis for the individual subject.

0087Output module <b>606</b> may be any suitable display, such as a monitor or may include graphs or reports relating to the obtained results. This information can either be used to detect effects of treatment on functional brain activity or to direct treatment, or it may be used for experimental or educational purposes. The analysis could be performed and presented offline or online during the sampling process. In one embodiment, output module <b>606</b> includes a feedback loop as part of a complete workstation (described below with reference to <figref idref="DRAWINGS">FIG. 24</figref>), wherein results from processor <b>604</b> are automatically used to provide additional stimuli to testing subject <b>603</b>. An example of a system using a feedback loop is presented in <figref idref="DRAWINGS">FIG. 24</figref>, which is a block diagram illustration of system <b>600</b> in accordance with one embodiment of the present invention.

0088In the example depicted in <figref idref="DRAWINGS">FIG. 24</figref>, system <b>600</b> is a workstation which may enable a professional (physical or occupational therapist, speech pathologist, rehabilitation doctor, neurologist, psychiatrist etc.) to observe and direct brain effects during treatment, either with current methods or with novel methods. The workstation incorporates information regarding identified functional patterns and their change prior and during practice from the above described technology together with input regarding the treatment protocols and their peripheral effects outside the brain sampled by various technological modalities. This enables an intelligent direction of the treatment either off-line or on-line during treatment. Part of the direction is based upon peripheral biofeedback or brain neurofeedback methods, which are pattern related. Virtual reality technology may also be incorporated in the work station.

0089As shown in <figref idref="DRAWINGS">FIG. 24</figref>, system <b>600</b> could include electrodes <b>13</b> placed on the head of testing subject <b>603</b>. Alternatively, any of the methods described above such as fMRI, PET, etc. could be used. Electrodes <b>13</b> are connected to amplifier <b>608</b>, for amplifying signals obtained by electrodes <b>13</b>, and sending the amplified signals to processor <b>604</b>. Processor <b>604</b> includes pattern comparator <b>612</b> and knowledge base <b>16</b>, as described above. Processor <b>604</b> provides output to output module <b>606</b>, which in the present embodiment includes a feedback loop provider <b>630</b>. Output module <b>606</b> with feedback loop provider <b>630</b> provides neurofeedback or peripheral feedback to subject <b>603</b> and is either a real-time on-line or alternatively off-line facilitator of stimuli wherein stimuli may be further provided or adjusted based on responses from testing subject <b>603</b>. In one embodiment feedback loop provider <b>630</b> includes virtual reality technology, wherein the subject <b>603</b> may be provided with multi-sensory input either for diagnosis, treatment or both.

0090An example of use of a system <b>600</b> including a feedback loop provider is as follows. The subject <b>12</b> may be asked to perform a particular task. If he is unsuccessful, feedback loop provider <b>630</b> receives data showing that the task was not successfully performed. Feedback loop provider <b>630</b> may then introduce multi-sensory stimulation either simulating the task to be performed or a similar task. Testing subject <b>603</b> may then be asked again to perform the particular task. If he is unsuccessful, the same inputs may be used again. If he is partially successful, either the same or new inputs may be used to encourage further performance of the task. In this way, the neurological or psychiatric function of the brain may be restored or enhanced in certain cases, or may be compensated for by activating other areas of the brain.

0091Sensory input by feedback loop provider <b>630</b> may include, for example, visual input <b>640</b>, somatosensory input <b>650</b>, auditory input <b>660</b> or any other sensory input that may aid in restoration of neurological activity. In some embodiments, one type of sensory input is used. In other embodiments, multiple sensory inputs are provided simultaneously or sequentially. It should be readily apparent that by observing the actual flow and by correlating the flow to particular activities or pathologies, the feedback loop can be greatly facilitated.

0092A system such as the one described can potentially be used for many neurological and psychiatric conditions such as rehabilitation of brain injuries, treatment of neurocognitive dysfunctions and treatment of behavioral and emotional pathologies and problems. It should be noted that non-clinical applications are also ample, such as analysis of decision making, analysis of mood, analysis of personality and in general analysis of any behavioral function.

0093Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims. All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention.

0094While certain features of the present invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the present invention.